Baowen Zhang, Chuan Fang, Rakesh Shrestha, Yixun Liang, Xiaoxiao Long, Ping Tan

Project page

git clone https://github.com/BaowenZ/RaDe-GS.git --recursive

1. create an environment

conda create -n radegs python=3.9
conda activate radegs

2. install pytorch and other dependencies.

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt

3. install submodules

pip install submodules/diff-gaussian-rasterization
pip install submodules/simple-knn/

We use preprocessed DTU dataset from 2DGS for training. And we follow GOF to evaluate the geometry. Point clouds from the DTU dataset need saved to dtu_eval/Offical_DTU_Dataset for the geometry evaluation.

# training
python train.py -s <path to DTU dataset> -m <output folder> -r 2 --use_decoupled_appearance
# mesh extraction
python mesh_extract.py -s <path to DTU dataset> -m <output folder> -r 2
# evaluation
python evaluate_dtu_mesh.py -s <path to DTU dataset> -m <output folder>

Current viewer in this repository is very similar to the original Gaussian Splatting viewer (with small modifications for 3D filters). You can build and use it in the same way as Gaussian Splatting.

We build this project based on Gaussian Splatting.

We adopt the loss functions of 2D GS and use the preprocessed DTU dataset.

We adopt the densification strategy, evalution and decoupled appearance modeling form GOF.

We thank the authors of Gaussian Splatting, 2D GS, and GOF for their great works.